Cytology Automation: An Overview

Valente, Philip T.; Schantz, H. Daniel

doi:10.1309/k4v5-7wpm-25df-hb6n

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Automated screening machines have been developed for analyzing cervical cytology smears, and a few FDA-approved automated primary screening device are available [ 17 ]. However, it is more difficult to develop an automated tool for cervical tissue histology due to the complexity of the patterns observed and the structural associations between different tissue components.…”

Section: Discussionmentioning

confidence: 99%

“…However, most recent studies have applied techniques for the detection and classification of invasive cancers [ 11 , 12 , 13 , 14 , 15 , 16 ] rather than intraepithelial or premalignant lesions. With regard to cervical lesions, some studies have been devoted to the creation of computer-assisted reading systems for assessing cervical cytology specimens [ 17 , 18 ] and only a limited number of studies have focused on examining CINs [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. In this study, we aimed to develop and assess an optimal convolutional neural network (CNN) model for classification of CINs.…”

Section: Introductionmentioning

confidence: 99%

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Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

et al. 2022

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Artificial intelligence has enabled the automated diagnosis of several cancer types. We aimed to develop and validate deep learning models that automatically classify cervical intraepithelial neoplasia (CIN) based on histological images. Microscopic images of CIN3, CIN2, CIN1, and non-neoplasm were obtained. The performances of two pre-trained convolutional neural network (CNN) models adopting DenseNet-161 and EfficientNet-B7 architectures were evaluated and compared with those of pathologists. The dataset comprised 1106 images from 588 patients; images of 10% of patients were included in the test dataset. The mean accuracies for the four-class classification were 88.5% (95% confidence interval [CI], 86.3–90.6%) by DenseNet-161 and 89.5% (95% CI, 83.3–95.7%) by EfficientNet-B7, which were similar to human performance (93.2% and 89.7%). The mean per-class area under the receiver operating characteristic curve values by EfficientNet-B7 were 0.996, 0.990, 0.971, and 0.956 in the non-neoplasm, CIN3, CIN1, and CIN2 groups, respectively. The class activation map detected the diagnostic area for CIN lesions. In the three-class classification of CIN2 and CIN3 as one group, the mean accuracies of DenseNet-161 and EfficientNet-B7 increased to 91.4% (95% CI, 88.8–94.0%), and 92.6% (95% CI, 90.4–94.9%), respectively. CNN-based deep learning is a promising tool for diagnosing CIN lesions on digital histological images.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

et al. 2022

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“…Automated screening machines have been developed for analyzing cervical cytology smears, and a few FDA-approved automated primary screening device are available [14]. However, it is more di cult to develop an automated tool for cervical tissue histology due to the complexity of the patterns observed and the structural associations between different tissue components [17].…”

Section: Discussionmentioning

confidence: 99%

“…However, most recent studies have applied techniques for the detection and classi cation of invasive cancers [8][9][10][11][12][13] rather than intraepithelial or premalignant lesions. With regard to cervical lesions, some studies have been devoted to the creation of computer-assisted reading systems for assessing cervical cytology specimens [14], and only a limited number of studies have focused on examining CINs [15][16][17][18][19][20][21]. In this study, we aimed to develop and assess an optimal convolutional neural network (CNN) model for classi cation of CINs.…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Cho

Kim

Park

et al. 2021

Preprint

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Artificial intelligence has enabled the automated diagnosis of several cancer types. We aimed to develop and validate deep learning models that automatically classify cervical intraepithelial neoplasia (CIN) based on histological images. Microscopic images of CIN3, CIN2, CIN1, and non-neoplasm were obtained. The performances of two pre-trained convolutional neural network (CNN) models adopting DenseNet-161 and EfficientNet-B7 architectures were evaluated and compared with those of pathologists. The dataset comprised 1,106 images from 588 patients; images of 10% of patients were included in the test dataset. The mean accuracies for the 4-class classification were 88.5% (95% confidence interval [CI], 86.3–90.6%) by DenseNet-161 and 89.5% (95% CI, 83.3–95.7%) by EfficientNet-B7, which were similar to human performance (93.2% and 89.7%). The mean per-class area under the receiver operating characteristic curve values by EfficientNet-B7 were 0.996, 0.990, and 0.971 in the non-neoplasm, CIN3, CIN1 groups, respectively. The class activation map detected the diagnostic area for CIN lesions. In the 3-class classification of CIN2 and CIN3 as one group, the mean accuracies of DenseNet-161 and EfficientNet-B7 increased to 91.4% (95% CI, 88.8–94.0%) and 92.6% (95% CI, 90.4–94.9%), respectively. CNN-based deep learning is a promising tool for diagnosing CIN lesions on digital histological images.

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“…The new methodology has led to the introduction of automatic and semiautomatic screening devices, e.g. BD FocalPoint [5], [6]. However, thus far, most of the proposed methods for computerized screening have been limited to dedicated scanners and to cervical screening.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in Lung Cytology

Forsberg

Monsef²

2014

2014 22nd International Conference on Pattern Recognition

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This paper presents results from an evaluation of three previously presented methods for segmentation of cell nuclei in lung cytology samples scanned by whole-slide scanners. Wholeslide images from seven cases of endobronchial ultrasound-guided transbronchial needle aspiration samples were used for extracting a number of regions of interest, in which approximately 2700 cell nuclei were manually segmented to form the ground truth. The segmented cells included benign bronchial epithelium, lymphocytes, granulocytes, histiocytes and malignant epithelial cells. The best results were obtained with a method based upon adaptive thresholding and an added step of clustering for distinguishing between cytoplasm and cell nuclei. This method achieved a mean DICE-score of 0.81 and a sensitivity and specificity of 0.88 and 0.81 respectively. In addition, this method was by far the fastest method, with a mean processing time of 7.8 s per image (2 048 × 2 048 pixels per image). By further improvements, such as lowering the false positive rate and using parallel computing hardware, this method has the potential to form the first building block in a system for computerized screening of whole-slide images in lung cytology.

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Cytology Automation: An Overview

Cited by 8 publications

References 17 publications

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in Lung Cytology

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